The present patent disclosure relates to the field of business intelligence and more particularly to the improvement of relevance of search results for business intelligence content.
Many businesses now generate and store information relating to all facets of the business. This information is often stored in data warehouse and databases. The amount of information stored can make finding relevant information difficult. However, timely intelligence is critical to proper business decision making.
A key to success in business today is therefore to understand and effectively manage the factors that drive an enterprise—a field known as business intelligence. Having critical Information about such business drivers allows decisions that will significantly improve results.
To facilitate better business intelligence decisions, organizations have developed data warehouses, which organize and link important customer information from a variety of data stores in a centrally accessible data repository. The data warehouses can include information gathered from various online transaction processing (OLTP) applications that record transactions and/or behaviors of customers when the customers interact with the organizations in some way. The information stored in the data warehouses may also include metadata information regarding the structure and relationship of the information. For example report specifications may be stored that describe information to be presented for a particular report. Additional information may be stored regarding relational models of the information, Dimensional models and Online Analytical Processing (OLAP) cube models as well as other information.
Moreover, organizations deploy a variety of business intelligence reports that allow the organization to use reporting tools, such as OLAP tools to create dimensions, metrics, filters, and templates associated with searching, retrieving, analyzing and viewing the organizations' data. The created dimensions, metrics, filters, and templates combine to form the business intelligence reports that process against the organizations' data in order to display results in tables and/or graphs. Further, the dimensions, metrics, filters, and templates are stored in a metadata format for use by a specific OLAP tool.
Current search and retrieval techniques in business systems focus on returning objects using universal full-text methods. Currently none of these systems optimize results based on how the different objects relate to each other and how they are used in a declarative system, either by the individual user or as a whole. Full-text search results typically need to be combed through in larger volumes and individually evaluated based on how well they match the underlying meaning of the query.
There is therefore a need for methods that better generate query based on the relationship of the different objects relevance of search results for business intelligence content.